Job Description
We are on the lookout for an Engineering Manager to join the Global Machine Learning Platform on our journey to always deliver amazing experiences. Our mission is to empower Data Science teams across Delivery Hero's diverse brands (Foodora, Foodpanda, Glovo, Talabat, Peya, Hungerstation, Woowa, and more) with a cutting-edge, scalable platform. This platform enables them to rapidly and reliably develop, deploy, and manage machine learning models that drive personalized experiences for our global customer base.
If you have a proven track record of leading high-performing teams and a passion for machine learning engineering, this role offers a unique opportunity to make a significant impact.
As a leader on our team, you will:
1. Lead & Mentor: Guide and empower a team of machine learning engineers to design, build, and maintain robust MLOps architecture, ensuring the ML lifecycle operates efficiently at scale.
2. Collaborate Strategically: Partner with consumer teams to deeply understand their objectives and identify potential roadblocks, ensuring our platform effectively addresses their needs.
3. Drive Product Vision: Collaborate closely with Product Managers to define and execute the product roadmap, accelerating the development of innovative ML solutions.
4. Deliver Impactful Products: Lead your squad of engineers in delivering critical ML Platform products, including tooling for model training, monitoring, serving, and deployment.
5. Foster Communication: Excel in stakeholder management, proactively bridging communication gaps to ensure seamless collaboration across teams.
6. Own Technical Excellence: Take ownership of technical decisions and execution, ensuring timely delivery of high-quality software solutions.
7. Cultivate High Performance: Define effective ways of working, establish clear expectations, provide constructive feedback, foster a strong team culture, conduct impactful 1:1s, and support individual growth plans to maintain a high-performing team.
Qualifications
8. Production ML Experience: Proven record of taking machine learning models from development to production and maintaining them at scale.
9. ML Engineering Expertise: Extensive experience in ML engineering or MLOps. Working knowledge of the ML lifecycle and its ecosystem of tools.
10. Cloud Proficiency: Prior experience working with public cloud platforms (GCP/AWS or equivalent) and knowledge of Kubernetes
11. Leadership Acumen: Proven experience leading and mentoring a team of engineers and engineering managers as a direct manager.
12. Innovation & Building: Experience in building new solutions from scratch.
13. Exceptional Communication: Fluent in English with strong written and verbal communication skills.
Nice to Have:
14. Experience collaborating with platform teams.
15. Familiarity with performance evaluation processes and developing growth plans for engineers.
16. Experience in cross-functional collaboration with data scientists, analysts, and product managers.
17. Practical experience with ML tools such as Metaflow, MLflow, Argo Workflows, and Cloud Notebooks.